Equivalence to add_nonlinear_constraint in new JuMP version

Specific Domains Optimization (Mathematical)
8

You don’t need to use Expr any more. Just do:

julia> using JuMP

julia> begin
           model = Model()
           @variable(model, ir1, start = 3.0)
           @variable(model, ir2, start = 3.0)
           @variable(model, ir3, start = 3.0)
           @variable(model, SP, start = 5.0)
           @variable(model, yA == 1.0)
           @variable(model, yF == 2.0)
           set_LHS = [ir1, ir2, ir3];
           set_RHS = [ir1, ir2, ir3, SP, yA, yF]
           set_func = Any[+, -, *, max, min]
           function make_RHS(set_RHS, set_func)
               return rand(set_func)(rand(set_RHS), rand(set_RHS))
           end
           function make_constr(set_LHS, set_RHS, set_func)
               return @constraint(model, rand(set_LHS) == make_RHS(set_RHS, set_func))
           end
       end
make_constr (generic function with 1 method)

julia> constr1 = make_constr(set_LHS, set_RHS, set_func)
-SP*ir3 + ir2 = 0

julia> constr2 = make_constr(set_LHS, set_RHS, set_func)
-yF*ir3 + ir3 = 0

julia> constr3 = make_constr(set_LHS, set_RHS, set_func)
ir1 - min(ir3, yF) = 0

julia> constr4 = make_constr(set_LHS, set_RHS, set_func)
ir3 - min(ir1, yA) = 0

julia> constr5 = make_constr(set_LHS, set_RHS, set_func)
ir2 - max(ir2, SP) = 0

julia> print(model)
Feasibility
Subject to
 ir1 - min(ir3, yF) = 0
 ir3 - min(ir1, yA) = 0
 ir2 - max(ir2, SP) = 0
 -SP*ir3 + ir2 = 0
 -yF*ir3 + ir3 = 0
 yA = 1
 yF = 2
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